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An Improved Approach to Conjoint Analysis for the Complex Decision-Making

机译:复杂决策的联合分析的一种改进方法

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The classical conjoint analysis (CA) has three drawbacks in computing preference values of the evaluated objects.Firstly, the three estimation methods of profile utilities may be invalid. Secondly, the three methods do not well reflect inherent interactive relations of complex systems. Thirdly, CA does not consider inaccurate characteristic of evaluators' judgments. Therefore,conclusions made by the classical CA are probably incorrect. To overcome these three drawbacks, according to the thought of the meta-synthesis from qualitative analysis to quantitative analysis of complex system theory, and based on the theory of the technique of fuzzy neural network, the improved approach to CA for the complex decision-making is presented. The numerical demonstration verifies that the developed approach is able to obtain rank of evaluated objects much closer to the real, and proves to be more reasonable than the classical CA.
机译:经典联合分析(CA)在计算被评估对象的偏好值时存在三个缺点:首先,剖面效用的三种估计方法可能是无效的。其次,这三种方法不能很好地反映复杂系统的固有交互关系。第三,CA不认为评估者的判断具有不正确的特征。因此,经典CA所做的结论可能是错误的。为了克服这三个缺点,根据从综合分析的定性分析到定量分析的综合理论,并基于模糊神经网络技术的理论,提出了一种改进的CA方法,用于复杂决策。被表达。数值算例验证了所开发的方法能够获得与实物更接近的评估对象等级,并证明比经典CA更合理。

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